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Low-rank matrix recovery with poison noise

Journal articles
Xie, Y; Chi, Y; Calderbank, R
Published in: 2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings
December 1, 2013
Published version (DOI)

Estimating an image M* +m1 ×m2 from its linear measurements under Poisson noise is an important problem arises from applications such as optical imaging, nuclear medicine and x-ray imaging [1]. When the image M* has a low-rank structure, we can use a small number of linear measurements to recover M*, also known as low-rank matrix recovery. This is related to compressed sensing, where the goal is to develop efficient data acquisition systems by exploiting sparsity of underlying signals. © 2013 IEEE.

Duke Scholars

Robert Calderbank
Author Robert Calderbank Computer Science
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Published In

2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings

DOI

10.1109/GlobalSIP.2013.6736959

Publication Date

December 1, 2013

Start / End Page

622
 

Citation

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Xie, Y., Chi, Y., & Calderbank, R. (2013). Low-rank matrix recovery with poison noise. 2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings, 622. https://doi.org/10.1109/GlobalSIP.2013.6736959
Xie, Y., Y. Chi, and R. Calderbank. “Low-rank matrix recovery with poison noise.” 2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings, December 1, 2013, 622. https://doi.org/10.1109/GlobalSIP.2013.6736959.
Xie Y, Chi Y, Calderbank R. Low-rank matrix recovery with poison noise. 2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings. 2013 Dec 1;622.
Xie, Y., et al. “Low-rank matrix recovery with poison noise.” 2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings, Dec. 2013, p. 622. Scopus, doi:10.1109/GlobalSIP.2013.6736959.
Xie Y, Chi Y, Calderbank R. Low-rank matrix recovery with poison noise. 2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings. 2013 Dec 1;622.

Published In

2013 IEEE Global Conference on Signal and Information Processing Globalsip 2013 Proceedings

DOI

10.1109/GlobalSIP.2013.6736959

Publication Date

December 1, 2013

Start / End Page

622